Unplanned downtime on pumps, motors, compressors, turbines and fleets costs far more than the repair itself, yet fixed-interval servicing replaces parts that still have life while missing those about to fail. Machine learning applied to sensor and maintenance records lets teams act when the evidence says so. Ten days take engineers and analysts from raw condition data to a deployed prediction service.
The first half covers maintenance strategies, reliability concepts, CMMS and SCADA data, vibration, temperature and current signals, cleaning and labelling failure histories, and feature engineering with Python, pandas and scikit-learn. The second half moves to anomaly detection, survival analysis, remaining useful life regression, time series and deep learning with LSTM networks, model evaluation for imbalanced failure data, explainability, MLOps, edge deployment and monitoring for drift. Labs use public turbofan, bearing and pump datasets and culminate in a capstone project. The programme suits maintenance and reliability engineers, condition monitoring specialists, plant and asset managers, data scientists in industry, and IoT and automation engineers in manufacturing, energy, mining, transport and utilities. Classroom, online and in-house delivery are offered, with a CPD-accredited certificate. You leave with a tested model and a business case for rollout.
Maintenance has shifted from fixing what breaks to anticipating what will break. Sensors, historians and maintenance management systems now generate more data than reliability teams can review by eye, and the opportunity lies in turning that data into early, trustworthy warnings. Organisations that do this well reduce emergency repairs, protect safety and plan spares and labour more effectively.
This ten-day programme builds the full capability, beginning with reliability engineering fundamentals and the data foundations that predictive models require. Participants learn how to assemble and clean time series from sensors and work orders, define failure events and labels, and engineer features from vibration spectra, thermal and electrical signals. They then train and compare classification, anomaly detection, survival and remaining useful life models, paying close attention to imbalanced data, leakage and validation by asset rather than by random split. The later days address interpretability, alert thresholds, cost-benefit analysis, integration with a CMMS, deployment pipelines and monitoring for drift.
Instruction is laboratory-based, using Python notebooks and open industrial datasets. In the final days participants work in teams on a capstone that moves from problem framing to a deployed model and a rollout proposal for a management audience.
By the end of the programme, participants will be able to:
Participants leave the programme with:
The programme is laboratory-driven and anchored in industrial data. Delivery includes:
Day 1: Maintenance Strategies and Reliability Basics
Day 2: Industrial Data Sources and Preparation
Day 3: Signal Processing and Feature Engineering
Day 4: Supervised Learning for Failure Prediction
Day 5: Anomaly Detection
Day 6: Remaining Useful Life Estimation
Day 7: Deep Learning for Sequences
Day 8: Evaluation, Explainability and Decision Value
Day 9: Deployment, Monitoring and Integration
Day 10: Capstone Project and Rollout Planning
The programme is suited to technical and managerial staff responsible for asset reliability and industrial data, including:
Participants who attend the ten days and complete the laboratory work and capstone project receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Predictive Maintenance with Machine Learning programme, you'll be able to compare reactive, preventive, condition-based and predictive maintenance strategies and select the right one, prepare sensor, scada and cmms data for modelling, including labelling failure events, engineer time-domain and frequency-domain features from vibration and process signals, and train and validate anomaly detection, failure classification and remaining useful life models. The full breakdown of topics is covered session by session in the Course Outline tab above.
The programme is suited to technical and managerial staff responsible for asset reliability and industrial data, including: Maintenance and reliability engineers, Condition monitoring and vibration analysts, Plant, asset and operations managers, Data scientists and analysts working with industrial data, IoT, automation and SCADA engineers, Fleet and workshop managers in transport and logistics, Engineers in power, water, mining and oil and gas facilities, Digital transformation and Industry 4.0 leads, and Engineering consultants and technical trainers.
Predictive Maintenance with Machine Learning Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Predictive Maintenance with Machine Learning Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Predictive Maintenance with Machine Learning starts October 12, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Predictive Maintenance with Machine Learning Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Predictive Maintenance with Machine Learning Training Course is pitched at advanced professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Predictive Maintenance with Machine Learning Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Predictive Maintenance with Machine Learning Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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